{"id":"W4403913151","doi":"10.1145/3678957.3688384","title":"HumanEYEze 2024: Workshop on Eye Tracking for Multimodal Human-Centric Computing","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Eye tracking; Human–computer interaction; Computer vision; Artificial intelligence; Computer graphics (images); Multimedia","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003507832,0.0002169534,0.0002152151,0.0003641642,0.0003350763,0.0004627609,0.000807344,0.0001364658,0.00004116103],"category_scores_gemma":[0.00007109664,0.0001878711,0.0001510228,0.0005932375,0.00005815665,0.0002004165,0.000183321,0.000370514,0.0001321841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008810507,"about_ca_system_score_gemma":0.00003492459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001136741,"about_ca_topic_score_gemma":0.00001024526,"domain_scores_codex":[0.9982232,0.00003100299,0.0002817287,0.0007535079,0.0002012335,0.0005092897],"domain_scores_gemma":[0.9989254,0.0004450671,0.00004960839,0.0004483629,0.00006504445,0.00006648284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004539313,0.0001588794,0.0004362766,0.00008550684,0.00005665263,0.0000799916,0.0005655573,0.0007270237,0.001674921,0.5610971,0.006407798,0.4287057],"study_design_scores_gemma":[0.0009999422,0.0003503151,0.01084204,0.0006267219,0.00003605835,0.00002512505,0.0002193075,0.9386196,0.005919297,0.01467055,0.02678972,0.0009013001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08023279,0.000275057,0.9087139,0.001969066,0.001451981,0.0003056468,0.000002399457,0.002367694,0.00468151],"genre_scores_gemma":[0.9672173,0.000003529604,0.02930193,0.0002394147,0.0002226365,0.00001535729,0.000004123711,0.00002284079,0.002972917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9378926,"threshold_uncertainty_score":0.7661163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03942858660742127,"score_gpt":0.3405728741669387,"score_spread":0.3011442875595174,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}